An Integrated Simulation, Inference and Optimization Approach for Groundwater Remediation with Two-Stage Health-Risk Assessment
Bibliographic record
Abstract
In this study, an integrated simulation, inference and optimization approach with two-stage health risk assessment (i.e., ISIO-THRA) is developed for supporting groundwater remediation for a petroleum-contaminated site in western Canada. Both environmental standards and health risk are considered as the constraints in the ISIO-THRA model. The health risk includes two parts: (1) the health risk during the remediation process and (2) the health risk in the natural attenuation period after remediation. In the ISIO-THRA framework, the relationship between contaminant concentrations and time is expressed through first-order decay models. The results demonstrate that: (1) stricter environmental standards and health risk would require larger pumping rates for the same remediation duration; (2) higher health risk may happen in the period of the remediation process; (3) for the same environmental standard and acceptable health-risk level, the remediation techniques that take the shortest time would be chosen. ISIO-THRA can help to systematically analyze interaction among contaminant transport, remediation duration, and environmental and health concerns, and further provide useful supportive information for decision makers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".